2,670 research outputs found
Automated Quantitative Description of Spiral Galaxy Arm-Segment Structure
We describe a system for the automatic quantification of structure in spiral
galaxies. This enables translation of sky survey images into data needed to
help address fundamental astrophysical questions such as the origin of spiral
structure---a phenomenon that has eluded theoretical description despite 150
years of study (Sellwood 2010). The difficulty of automated measurement is
underscored by the fact that, to date, only manual efforts (such as the citizen
science project Galaxy Zoo) have been able to extract information about large
samples of spiral galaxies. An automated approach will be needed to eliminate
measurement subjectivity and handle the otherwise-overwhelming image quantities
(up to billions of images) from near-future surveys. Our approach automatically
describes spiral galaxy structure as a set of arcs, precisely describing spiral
arm segment arrangement while retaining the flexibility needed to accommodate
the observed wide variety of spiral galaxy structure. The largest existing
quantitative measurements were manually-guided and encompassed fewer than 100
galaxies, while we have already applied our method to more than 29,000
galaxies. Our output matches previous information, both quantitatively over
small existing samples, and qualitatively against human classifications from
Galaxy Zoo.Comment: 9 pages;4 figures; 2 tables; accepted to CVPR (Computer Vision and
Pattern Recognition), June 2012, Providence, Rhode Island, June 16-21, 201
A Fisher-Rao metric for paracatadioptric images of lines
In a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but
the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it diffcult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied.
The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and
based on the Hough transform
3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation
This paper proposes a simple and efficient method for the reconstruction and
extraction of geometric parameters from 3D tubular objects. Our method
constructs an image that accumulates surface normal information, then peaks
within this image are located by tracking. Finally, the positions of these are
optimized to lie precisely on the tubular shape centerline. This method is very
versatile, and is able to process various input data types like full or partial
mesh acquired from 3D laser scans, 3D height map or discrete volumetric images.
The proposed algorithm is simple to implement, contains few parameters and can
be computed in linear time with respect to the number of surface faces. Since
the extracted tube centerline is accurate, we are able to decompose the tube
into rectilinear parts and torus-like parts. This is done with a new linear
time 3D torus detection algorithm, which follows the same principle of a
previous work on 2D arc circle recognition. Detailed experiments show the
versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing,
Sep 2015, Genova, Italy. 201
3D spherical-cap fitting procedure for (truncated) sessile nano- and micro-droplets & -bubbles
In the study of nanobubbles, nanodroplets or nanolenses immobilised on a
substrate, a cross-section of a spherical-cap is widely applied to extract
geometrical information from atomic force microscopy (AFM) topographic images.
In this paper, we have developed a comprehensive 3D spherical cap fitting
procedure (3D-SCFP) to extract morphologic characteristics of complete or
truncated spherical caps from AFM images. Our procedure integrates several
advanced digital image analysis techniques to construct a 3D spherical cap
model, from which the geometrical parameters of the nanostructures are
extracted automatically by a simple algorithm. The procedure takes into account
all valid data points in the construction of the 3D spherical cap model to
achieve high fidelity in morphology analysis. We compare our 3D fitting
procedure with the commonly used 2D cross-sectional profile fitting method to
determine the contact angle of a complete spherical cap and a truncated
spherical cap. The results from 3D-SCFP are consistent and accurate, while 2D
fitting is unavoidably arbitrary in selection of the cross-section and has a
much lower number of data points on which the fitting can be based, which in
addition is biased to the top of the spherical cap. We expect that the
developed 3D spherical-cap fitting procedure will find many applications in
imaging analysis.Comment: 23 pages, 7 figure
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